Efficient and lightweight 3D building reconstruction from drone imagery using sparse line and point clouds

Abstract

Efficient three-dimensional (3D) building reconstruction from drone imagery often faces data acquisition, storage, and computational challenges because of its reliance on dense point clouds. In this study, we introduced a novel method for efficient and lightweight 3D building reconstruction from drone imagery using line clouds and sparse point clouds. Our approach eliminates the need to generate dense point clouds, and thus significantly reduces the computational burden by reconstructing 3D models directly from sparse data. We addressed the limitations of line clouds for plane detection and reconstruction by using a new algorithm. This algorithm projects 3D line clouds onto a 2D plane, clusters the projections to identify potential planes, and refines them using sparse point clouds to ensure an accurate and efficient model reconstruction. Extensive qualitative and quantitative experiments demonstrated the effectiveness of our method, demonstrating its superiority over existing techniques in terms of simplicity and efficiency.